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DOCUMENT
DE TRAVAIL
N° 393
DIRECTION GÉNÉRALE DES ÉTUDES ET DES RELATIONS INTERNATIONALES
EUROPEAN EXPORT PERFORMANCE
Angela Cheptea, Lionel Fontagné and Soledad Zignago
August 2012
DIRECTION GÉNÉRALE DES ÉTUDES ET DES RELATIONS INTERNATIONALES
EUROPEAN EXPORT PERFORMANCE
Angela Cheptea, Lionel Fontagné and Soledad Zignago
August 2012
Les Documents de travail reflètent les idées personnelles de leurs auteurs et n'expriment pas nécessairement la position de la Banque de France. Ce document est disponible sur le site internet de la Banque de France « www.banque-france.fr ». Working Papers reflect the opinions of the authors and do not necessarily express the views of the Banque de France. This document is available on the Banque de France Website “www.banque-france.fr”.
European Export Performance ∗
Angela Cheptea† Lionel Fontagne‡ Soledad Zignago§
∗We would like to thank the participants in the Banque de France Seminar, the PSE-GmonD Conferenceon Quality and Trade, and the XII Conference on International Economics (Castellon), as well as GuillaumeGaulier, Luciana Juvenal and Julia Woerth for their comments. This is a profoundly revised and extendedversion of CEPII Working Paper 2010-12. The views are those of the authors. The usual disclaimer applies.Tables of this paper present results only for large exporters. Results for all countries in the world are availablein our personnal webpages.†INRA Rennes ([email protected]).‡PSE (Universite Paris 1), European University Institute, Banque de France and CEPII
([email protected]).§Banque de France ([email protected]).
Abstract
Competitiveness has come to the forefront of the policy debate within the European Union,focusing on price competitiveness and intra-EU imbalances. But how to measure compet-itiveness properly, beyond price or cost competitiveness, remains an open methodologicalissue; and how can we explain the resilience of producers located in the EU to the compe-tition of emerging economies? We analyze the redistribution of world market shares at thelevel of the product variety, as countries no longer specialize in sectors or even products, butin varieties of the same product, sold at different prices. We decompose changes in marketshares into structural effects (geographical and sectoral) and a pure performance effect. Ourmethod is based on an econometric shift-share decomposition and we regard the EU-27 as anintegrated economy, excluding intra-EU trade. Revisiting the competitiveness issue in such aperspective sheds new light on the ongoing debate. From 1995 to 2009 the EU-27 withstoodthe competition from emerging countries better than the US and Japan. The EU marketshares in the upper price range of the market proved quite resilient, by combining goodperformance and favorable structure effects, unlike the US and Japan. Finally, while mostdeveloped countries lose market shares in high-technology products to developing countries,the EU is slightly gaining, benefiting of a favorable structure effect.
Keywords: International Trade, Export Performance, Competitiveness, Market Shares,Shift-Share, European Union.
JEL classification codes : F12, F15.
Resume
La specialisation des pays ne se fait plus au niveau des produits ou des secteurs, mais auniveau des varietes d’un meme produit (vendues a des prix differents). Pour etudier lamaniere dont l’UE fait face a l’emergence de nouveaux grands exportateurs mondiaux, Chineen tete, nous analysons la redistribution mondiale des parts de marche dans ce contexte re-nouvele. Pour distinguer ce qui releve de la performance de chaque exportateur des positionsqu’il a acquises sur les differents marches, nous decomposons les changements observes dansses parts de marche (commerce mondial de biens hors intra-UE) en effets structurels (geo-graphique et sectoriel) et un pur effet de performance. De 1994 a 2009, l’Union a 25 resistemieux que les Etats-Unis et le Japon a la concurrence des emergents. Contrairement auxautres economies avancees, l’Europe gagne des parts de marche dans les produits de haute-technologie et maintient sa place de leader mondial dans le haut de gamme grace a un bonpositionnement dans les secteurs les plus demandes ainsi qu’a une assez bonne performancecommerciale.
Mots-cle : Commerce international, Performance a l’exportation, Parts de marche,Analyse a parts de marche constantes, Shift-Share, Union europeenne.
Codes classification JEL : F12; F15.
2
1 Introduction
The 2020 European Agenda focuses explicitly on issues of competitiveness. Though the EU
officially defines competitiveness in the broad sense as an economy’s capacity to grow with full
employment in a sustainable way (with respect to environmental and social pillars/aspects),
the ongoing European debate on competitiveness is much more narrowly focused. Internal
current account imbalances within the EU, arguably explained by a divergence in price or
cost competitiveness between Member States, are the central concern. Notwithstanding
legitimate concerns regarding macroeconomic imbalances having fueled the debt crisis, such
an approach is however questionable for two reasons.
First, assessing competitiveness accurately is a challenging issue as most of the action
is taking place on the front of non-price competitiveness and is potentially affected by the
products or destination markets exporters specialize in. For instance, Italy has exhibited poor
price competitiveness over the recent years, but with resilient market shares. In contrast, the
improvement in Japanese price competitiveness did not prevent the deterioration of its world
market shares. More fundamentally, the effective demand introduced into macroeconomic
equations is by construction missing the sectoral or product dimension. Quality positioning,
sectoral specialization and geographical orientation of exports all contribute to the observed
changes in market shares.
Second, what ultimately matters for the EU as a whole, and more generally for high-
income countries, is the capacity to withstand competition from emerging economies and low
wage countries.1 This broader perspective is justified by the fact that emerging countries
have been winning large market shares over the last two decades. Among these, China
stands out with the most remarkable performance: it has almost trebled its world market
share since 1995, reaching 17.1% in 2009. This competitive pressure is striking for the most
technological products, where many of the new competitors have combined an increase in
market share with a higher unit value of the exported products.
Our aim in this article is to break down observed changes in market shares into prod-
1Interestingly, this view is not absent from the EU Commission philosophy, as the Directorate Generaltrade action is guided by the axiom: To build a stronger EU economy at home, Europe has to be morecompetitive abroad. The US Department of Commerce uses a similar definition and focuses on maximizingUS competitiveness by enabling economic growth for American industries, workers, and consumers.
3
uct or geographical specialization of exporters, and into pure performance. We develop an
econometric shift-share decomposition of export growth that identifies for each exporter the
contribution to the intensive margin of (i) the composition of its exports by product and des-
tination and (ii) its competitiveness. Accordingly, export growth for each country is broken
down into three components: a geographical composition effect, a sectoral composition effect
and an exporter effect capturing other sources of country’s export performance, including
competitiveness. In line with a now abundant literature, we measure export performance at
the level of the (vertically differentiated) variety of the traded products (Schott 2004, Hallak
2006, Baldwin & Ito 2008, Fontagne et al. 2008, Manova & Zhang 2011, Khandelwal 2010,
Hallak & Schott 2011). We also focus on high-tech products. We adopt the viewpoint of
an integrated European market and reconstruct world trade excluding intra-EU trade flows.
The latter are considered as “intranational” trade.2
The method we use yields several improvements with respect to the standard Constant
Market Share (CMS) decomposition found in the literature (Tyszynski 1951, Richardson
1971a,b, Bowen & Pelzman 1984, Fagerberg 1988).3 First, the competitiveness effect is esti-
mated rather than computed as a residual of the analysis. Second, the econometric approach
makes it possible to eliminate the non-orthogonality of product and market structure effects
in standard CMS analyses, responsible for the fact that the order of the decomposition
changes the results. In addition, we are able to identify confidence intervals for each prod-
uct, market and exporter effect. Unlike the standard approach, our methodology enables us
to obtain results (effects) that are additive over the time dimension and thus take stock of
changes in countries’ initial export structure.
To proceed, it is necessary to utilise very detailed and longitudinal trade data, covering all
countries, including information on bilateral trade unit values. To this end, we make use of a
database of international trade at the product level – BACI – developed by Gaulier & Zignago
(2010). BACI provides (FOB) reconciled values, as well as unit values (values/quantities), of
all international trade flows for about 5,000 product headings from the 6-digit Harmonised
267% of EU 27 exports are within the Single European Market, where most European countries recordlarger market shares thanks to better market access.
3Alternative measures of country competitiveness have been used in the literature: comparative advan-tage, specialisation or productivity indicators, cost of leaving indices (Fagerberg 1988, Neary 2006, Delgadoet al. 2012).
4
System classification (hereafter HS6) – since 1994. We consider all traded products, i.e. the
primary and manufacturing sectors, with the exception of mineral products, notably oil, as
well as some specific and non classified sectors. The availability of unit values enables us to
classify flows by price range and thus to analyze the positioning of exporters by price segment.
We employ these data to examine changes in market shares of leading world exporters over
the period 1995-2009. The world distribution of unit values for each HS6 heading allows us
to classify each product-bilateral flow into three price segments, and to examine competition
within each of these segments.
In the context of a major reshaping of world trade flows since the mid-1990s, we conclude
that the redistribution of market shares observed between emerging and developed countries
and among developing countries themselves has affected the EU, Japan and the US differ-
ently. European market share losses arise mainly during the first half of the period (up to
2001) and mostly concern long-standing Member States. The EU’s overall good performance
over the 1995-2009 period – compared to the United States or Japan – is associated with
an original price-quality positioning of its products. The EU has gained market shares in
the upper price range of the market by combining good performance and favorable structure
effects, unlike the US and Japan which have withdrawn extensively from this segment. Fi-
nally, all developed countries lose market shares in high-technology products to developing
countries, with the EU losing less than other countries.
The rest of the paper is organized as follows. We review the redistribution of world market
shares in Section 2, with a focus on high-tech and top range products. Our econometric shift
share analysis of export growth is implemented in Section 3. Section 4 concludes.
2 The redistribution of world market shares between
1995 and 2009
The objective of this section is to take stock of the recent shifts in world market shares,
taking into account the price segment and technological content of exported products at the
most detailed available level of classification of traded products. We firstly characterize the
5
extensive and intensive margins of world trade, then we examine what have been the big
changes in market shares, and we conclude with a focus on top range and high-tech products.
2.1 Changes in trade margins
Trade can increase either by exchanging a larger value of already traded products between
the same partners (the intensive margin of trade), or by increasing the number of countries
involved and/or exchanged products (the extensive margin of trade). The former refers to
the change in the value of existing trade flows, while the latter refers to the change in the
composition of trade flows. The entry of new competitors is reflected in the margins of world
exports at the most disaggregated level of the product classification.4 Hummels & Klenow
(2005) use a cross-section of detailed trade data to identify the patterns of exports of 126
countries in 1995, and find that 60% of large economies’ export growth is attributable to
shipments of a wider set of goods and the remaining 40% to larger quantities and higher
prices of each good already shipped.
We adopt a similar approach but use the most detailed trade data compatible with
an exhaustive set of exporters to compute the two margins for the whole matrix of trade
flows.5 Drawing on information by product, market, exporter, and year, we compute the
extensive margin of trade, defined as the change in the number of trade flows at the most
detailed level, or as the net value of appearing and disappearing trade flows. Symmetrically,
the intensive margin of trade is defined as the change in the value of trade flows that are
present continuously throughout a given period. While a rapid turnover of trade flows can
be observed – in a world matrix mostly full of zeros – the largest contribution to the growth
in the world trade value has been on the intensive margin.
Let us firstly consider the number of potential trade flows. A simple calculation would
compare the 3.6 million trade flows observed in 1995 (see Table 1, Panel 1) with a potential
4The extensive margin of exports so defined should not be confused with the heterogeneous firms settingswhere trade introduces a selection between firms, as well as, in case of multi-product firms, a selection withinthe portfolio of products of each exporter.
5Hummels & Klenow (2005) draw on HS6 data on exports in 1995 by 110 countries to 59 importers.Alternatively, they use US imports from 119 countries in over 13,000 10-digit US tariff lines for the sameyear. Our approach also differs from Besedes & Prusa (2011) who integrate the time dimension into theanalysis of export growth and breakdown the intensive margin into a survival and a deepening component.
6
of some 200 countries trading on a bilateral level in some 5,000 products. Accordingly, only
a tiny percentage of the whole universe of trade flows would have been observed. However,
simply taking the number of products times the number of exporters times the number of
importers is misleading: most products are not exported by every country. Thus, we must
compute this potential number by restricting it to situations where a product is at least
exported by one country to one partner. Thus, for each year and product if a country
reports its trade with at least one partner, trade flows with all unreported destinations are
considered as true zeros and correspond to potential flows. Under this assumption, we get
some 74 million potential trade flows in 1995 and 88 million in 2007. Accordingly, only 4.9
percent of the potential trade flows were actually observed in 1995 and 6.4 percent in 2007.
The change in the number of countries is not the explanation of such increase: what matters
is the product diversification of their exports.
Using the set of observed flows in Table 1 we compute the intensive and extensive change
in the value of world trade between 1995 and 2009. In panel (1) of this Table we start by
excluding mineral products, specific and non-classified products.6. The observed USD 4,204
bn 1995-2009 increase in world trade (column C) can be decomposed into three components.
Firstly, the 2.3 million elementary bilateral trade flows recorded in 1995 and still in place
in 2009 (second line of Table 1) have increased their value by USD 3,428 bn. Accordingly,
the intensive margin accounted for 81.6% of the change in the value of world trade (ratio of
column D to column C). Secondly, one third of 1995 trade flows (1.34 million flows) have
disappeared by 2009. This is the result of firms and countries ceasing trade with certain
markets or certain products. In 1995 these trade flows amounted to USD 289 bn. Lastly, 3.07
million new country-partner-product trade flows appeared during the period, corresponding
to the positive extensive margin of trade. This is a very large number, exceeding the number
of initial trade flows. Overall, only 42.7% of the number of trade flows recorded in 2009 were
already present in 1995. The remaining 57.3% are new flows (column E) either in terms of
destination, exported products, or both. Meanwhile, the contribution of new entries to the
1995-2009 growth of trade in value terms amounted to only 14.4%. Exits (column F) account
6We exclude HS chapters 25, 26, 27, 97, 98, and 99 all throughout this paper, as detailed in Section 5.1in the Appendix.
7
for 25.1% of the number of 1995 flows but only for 3.9% of their value. Thus, although the
exports of new products and/or exports to previously unexploited markets account for a
large share of the total number of flows both in 1995 and 2009, they represent much less
(10.5%) of the value increase in global trade.
Table 1: Extensive and intensive margins in world trade, 1995-2009
Unit 1995 2009 ∆ Intensive Extensive
A B C= B-A D E F G =E-F(D+G) Entries Exits Net
Data at the HS 6-digit level :
All flows, USD bn 3,197 7,400 4,204 3,428 1,065 289 776intra-EU excl. nb flows, 1000 3,629 5,354 2,286 3,068 1,343 1,725
Data aggregated at the HS 2-digit level :
(1) All flows, USD bn 3,197 7,400 4,204 3,935 298 29 269intra-EU excl. nb flows, 1000 369 526 289 236 80 156
(2) Our (reduced) USD bn 3,179 7,339 4,159 4,095 353 289 64sample nb flows, 1000 270 384 3,904 933 818 115
Source: Authors’ calculations using BACI values (current USD) of traded goods. Horizontal panel (1)combines all trade flows, excluding intra-EU trade and mineral, specific, and non-classified products.Horizontal panel (2) is obtained from panel (1) by excluding non-independent territories, micro-statesand small flows (<10,000 USD). For each panel, we give figures in billion dollars and in thousands ofHS6 or HS2 bilateral flows.
These results can be qualified by performing some sensitivity tests. Let us first aggregate
trade flows at the HS 2-digit level. This indeed yields a considerably lower number of flows
in each column of Table 1 and a larger relative importance of the intensive margin. The
USD 4,204 bn increase in world trade decomposes as follows: 93.6% for the increase in the
value of trade flows that survived throughout the period, 7.1% for new flows (entries), and
0.7% for trade flows that disappeared by 2009 (exits). Next, we can exclude non-independent
territories and micro-states7 as well as small flows (below USD 10,000), which account for a
large share of the total number of individual bilateral trade flows but a very limited share of
their value. These small flows are also excluded in section 3. When one combines these two
7Non-independent territories and certain small countries do not collect and report data on their foreigntrade separately. We keep however Taiwan and Macao due to the large value of their trade.
8
corrections, we end up with a contribution of the extensive margin of 6.4% (267/4,159, figures
not reported in Table 1), pointing to the robustness of our findings. Finally, in line with the
methodology developed in Section 3, we may also choose to compute the intensive margin as
the sum of annual changes in trade flows present in any two consecutive years rather than the
change in the value of flows present in 1995 and 2009. The resulting extensive margin (panel
(2) of Table 1) accounts only for a small fraction (1.5%=64/4,159) of the overall change in
trade, which allows us to use a decomposition of changes in market shares based on the
intensive margin only.
The contribution of the different margins of trade can be computed for individual large
exporters. Table 8 in the appendix compares the EU to other large exporters from the
developed and the developing world. Computations are performed at the country level. For
ease of presentation, as well as in the rest of the paper, results for countries that account for
less than 1% of world exports from 1995 to 2009 are aggregated within three groups – the
Middle East and North Africa (MENA), Sub-Saharan Africa (SSA), and Rest of the World
(RoW). Results for all other countries are available in our online appendix.8 We observe
that the contribution of the positive extensive margin (entries) to the growth of the value
of exports is very similar for the developed economies (less than 4%). This points to the
pronounced inertia in the exports of the advanced economies, particularly the US, Germany,
UK, and Japanese exports. Their trade growth is mainly accounted for by expansion in
existing markets (98.9%, 99.7%, 99.6% and 99.7% respectively). The contribution of the
positive extensive margin is larger for emerging economies. It peaks for instance at 65.7%
for Ukraine, 54% for Russia, and 25% for Greece. On average, the contribution of new
flows in export growth for countries not reported in Table 8 is 32%, clearly in excess of
the individual exporters reported in the Table (for the Middle East and North Africa this
contribution is 30% and for Sub-Saharan Africa 16%). The lowest shares among developing
countries are observed for China and Mexico, which show a structure of export growth similar
to the developed exporters. Mexico reaped the benefits of its preferential access to the huge
US market, but did not manage to diversify its portfolio of products or markets over the
considered period. In contrast, results for China also confirm the magnitude of the increased
8Zipped file at Soledad Zignago’s Banque de France webpage and Lionel Fontagne’s personal webpage.
9
intensive margin, but the diversification of their exports was already accomplished in 1995
(China ships roughly as many different products as Germany).9
How did the different EU Member States behave in terms of the two margins of trade?
Did the new Member States perform better in the extensive margins of trade than long-
standing Member States? Country level results show that the latter increased their exports
mainly within their already established trade relationships. The relative importance of the
intensive margin goes from 39.5% for Bulgaria to 99.9% for Finland (results available on our
online appendix). For Denmark and Cyprus the negative extensive margin (exits) exceeded
the positive one (entries), yielding a contribution of the intensive margin that was greater
than 100%. By contrast, new members’ export growth is achieved much more by developing
new trade relationships. The contribution of the positive extensive margin to the growth
of exports exceeds 18% for Baltic countries (reaching 40.2% for Latvia) and Malta. Among
the 15 long-standing Member States only Greece exhibits comparable figures. Since export
baskets and destinations of the new EU members were profoundly reshaped during the 1995-
2009 period, the negative extensive margin is also larger for these countries. Nonetheless, the
net extensive margin always accounts for less than half of the growth in countries’ exports.
In Section 3 we decompose the intensive margin of exports using an econometric shift-
share methodology. Our objective is to use this decomposition to identify the changes in the
determinants of the good resilience of EU market shares in the upper segment of the market.
2.2 EU market shares compared with main world exporters
In Table 2, we summarise the recent shifts in world market shares as follows. The first three
columns give the market share in 1995, 2007 (before the trade collapse), and 2009. In the
three subsequent columns, we report the percentage point changes in market shares for the
whole period and for the two sub-periods (1995-2007 and 2008-2009).
The most remarkable development in Table 2 is that China has more than doubled its
world market share (its market share in 2009 was 2.7 larger than in 1995), becoming larger
9Wang & Wei (2010) use export at product level for different Chinese cities and point to the role ofhuman capital and government intervention in shaping a specialisation that increasingly overlaps with thatin high-income countries.
10
Table 2: Changes in world market share for the world’s largest exporters, 1995-2009
Market shares, % ∆, p.p.
Exporter 1995 2007 2009 1995-2009 2007-2009
EU 27 20.7 19.5 19.4 -1.30 -0.09France 2.8 2.3 2.5 -0.38 0.16Germany 5.6 5.5 5.5 -0.16 -0.07Italy 2.7 2.3 2.3 -0.43 -0.02UK 2.8 2.0 1.9 -0.89 -0.09Euro Area 12 15.7 14.9 14.9 -0.79 0.03
USA 18.3 13.0 12.5 -5.76 -0.51Japan 14.2 8.9 8.0 -6.17 -0.86Canada 5.3 3.8 3.1 -2.17 -0.75Switzerland 2.8 2.3 2.4 -0.37 0.18
China 6.3 15.5 17.1 10.80 1.58Brazil 1.4 1.7 1.7 0.29 0.02India 1.1 1.7 2.1 1.02 0.40Indonesia 1.2 1.2 1.3 0.11 0.05Korea 3.8 4.4 4.7 0.89 0.32Malaysia 2.4 2.1 2.1 -0.29 -0.01Mexico 2.2 2.8 2.7 0.46 -0.13Taiwan 3.7 3.6 3.3 -0.44 -0.31Singapore 2.8 2.0 2.0 -0.73 0.02Thailand 1.8 1.9 2.1 0.32 0.18
MENA 2.5 4.0 3.9 1.44 -0.10Sub-Saharan Africa 1.5 1.6 1.6 0.06 -0.04RoW 8.1 9.9 10.0 1.84 0.03
Source: Authors’ calculations using BACI values (current USD) of tradedgoods. We exclude oil and intra-EU trade. The change in market shares isgiven in percentage points (p.p.). Results for countries accounting for lessthan 1% of world exports from 1995 to 2009 are aggregated within threegroups: the Middle East and North Africa (MENA), Sub-Saharan Africa(SSA), and Rest of the World (RoW).
11
than the US as a super trader. In 1995, EU 27 had a 20.7% market share of the world
trade in goods (excluding intra-EU flows). This market share has been only slightly affected
by competitive pressures from emerging economies, falling to 19.4% in 2009. Thus, the EU
market share has been fairly unaffected by the eleven-point rise in China’s share over the
same period. In contrast, Japan and the US lose around 6 percentage points of market share
each.
The EU’s export performance varies significantly between markets. The EU shows a
decrease in market shares on some of the most dynamic importing markets during the last
decade.10 The largest gain is in the US market, where the EU accounted for over one fifth of
the import market in 2007. This performance coincided with shrinking shares of Japanese
and, to a lesser extent, of Canadian and ASEAN exports in the same market. Conversely,
the EU loses market shares on the Japanese and BRICs markets. The small market share
loss of EU products on the rapidly expanding Chinese market could, however, have a large
impact in the long run.
Like the other emerging countries, the new European Member States are doing better
than the EU15. This may be linked to a shift of production lines from EU industrialised
countries to new Member States with lower costs. The exception is Ireland, which has been
the most successful exporter among the EU-15 group over the period, doubling its world
market share. Poland, Hungary, Slovakia, and the Czech Republic also recorded large gains
in market shares. By contrast, the UK, Sweden, Italy, and Finland and France experienced
the greatest losses in their world market shares, as well as Cyprus and Bulgaria on the new
Members States side.
Changes in market shares also vary across sectors as illustrated in Table 9 of the Ap-
pendix, which provides the sectoral composition of world and EU exported values and their
evolution between 1995 and 2009, in current and constant terms.11 Among the best per-
forming sectors in terms of world values, the manufacture of basic metals, chemicals and
machinery stand out. However, in the case of chemicals and basic metals, their increased
weight in the world market is largely explained by price effects (comparison between columns
10Results not shown in the paper but available upon request.11Values are converted into volumes using chained Tornqvist indices of unit values. See the data appendix
for more details on the sources and methodologies used.
12
(5) and (6) of Table 9), which can be linked to the impact of oil price developments for these
two industries. Conversely, changes in machinery, radio, TV and other communication equip-
ment, as well as in medical, precision and optical instruments are strong in terms of volumes
than in values. The sectoral redistribution of European exports during the period favoured
chemicals but also the automotive industry, for which the increase in volume terms is larger
than in values. Food, beverages, textiles, apparel, basic metals and computers are among
sectors recording the largest losses in their share of European exports.
Figure 1: Changes in world market shares, 1994-2009
0
2
5
10
15
20
1994 1997 2000 2003 2006 2009
EU 27
USA
Japan
China
MENA
Brazil
India
Source: Authors’ calculations using BACI values (current USD) of traded goods. Oil and intra-EUtrade is excluded.
This redistribution of market shares must be gauged against the backdrop of the U-shaped
curve of the euro-US dollar exchange rate over the period. In Figure 1 we plot the evolution
of world market shares for selected exporters, also summarised in columns 1 to 3 of Table 2.
The EU’s market shares decreased more during the late 90s than in the early 2000s. Despite
the appreciation of the euro, the early 2000s were a period of partial recovery for the EU’s
exports, with most of its previous losses recuperated. Among other industrialised countries,
Japan continued to lose market shares in the second sub-period. All of the US losses are also
concentrated in that period. The competitive pressure from China has increased since 2000,
13
and not all emerging markets have managed to cope with this.12
Overall, the economic crisis has not changed the redistribution of world market shares
among global exporters. The last column of Table 2 gives the percentage point change
in the two-year-period covering the great trade collapse13, 2008-2009. The crisis seems to
confirm the long-run trends above mentioned: China’s performance (+1.6 p.p. gain in world
market share between 2007 and 2009), the vulnerability of Japanese and North-American
exporters and the resilience of Europe. The online appendix shows that the main changes
observed between the period 1995-2007 and the period 2008-2009, stem from the sectoral
composition of demand. Whereas transformed products gain market shares in the 1995-2007
period, the crisis collapsed demand for them. Conversely, consumption goods more than
compensate their previous losses in the last two years. In terms of technological content,
resource-based and mid-tech manufactures have recorded the big losses during the crisis,
to the benefit of primary products and to high-tech manufactures. The next sub-section
details the technological dimension of larger exporters specialization and addresses another
dimension of international competition: performances differ within categories of products
according to the market positioning of varieties. This is what is fundamentally important
for European exporters.
2.3 Performances in high-tech and top range products
High-tech and top range quality products play an important role in international competition,
since they are basically the output of innovation and the real source of rents. Leamer (1987)
pioneered the idea that what you export matters. Hausmann et al. (2007) went one step
further by characterizing the proximity of specialization between advanced and emerging
countries at the HS6 product level. They show that the “income level of a country’s exports”
is a determinant of subsequent growth.
We first focus here on high-tech products and use the classification proposed by Lall
(2000). Sectors are classified into primary products, resource-based manufactures, low,
12For instance, results available in our online appendix show disappointing performances for Mexico andASEAN countries since 2000.
13Record negative export growth rates were attained between the last quarter 2008 and the first half 2009for most countries in the world.
14
medium and high-technology manufactures, and other transactions. The high-tech category
comprises electronics and electrical products, as well as pharmaceutical products, aerospace,
optical and measuring instruments, cameras, etc. (see Table 7 in the Appendix for the sectors
classified in the other categories).
Results concerning high-tech products are reported in the first two columns of Table 3.
The first one gives the world market shares for high-tech products in 2009, the second one
their change in percentage points over the period 1995-2009. The EU has gained market
share in high-tech products: a 1.55 p.p. gain compared to a loss of 1.30 p.p. for all products
taken together (column 4 of Table 2). The United States and Japan, on the other hand,
recorded losses twice as large as for all products (respectively 10 p.p. and 12 p.p., as shown
in the second column of Table 3). In the meantime, Chinese gains are very large on the
high-tech market (17 p.p.), due to a massive relocation of the assembly of these products to
mainland China.
Besides trade similarity in terms of product categories, trade flows with persistently
dissimilar prices can be observed within the most narrowly defined products. Though high-
income and emerging economies export quite similar bundles of goods, they actually compete
within industries, on different price-quality ranges (Schott 2004, 2008, Fontagne et al. 2008).
Hence, specialization occurs within these categories, on vertically differentiated varieties of
products. However, quality is not directly observable. Hallak (2006) refers to product quality
as a demand shifter that captures all the attributes of a product valued by consumers.
Conditional on price, a higher quality increases income share spent on a given variety. Using
this definition, he finds that cross-country variation in unit values can be attributed to
differences in quality. Competitiveness ultimately depends upon the quality-adjusted price
(Baldwin & Harrigan 2011). Baldwin & Ito (2008) classify products according to the related
market structures (price competition versus quality competition) for nine big exporters in
the period 1997-2006. Estimating the price-distance relationship separately for each product,
they observe more “quality-competition goods” in EU exports than in US and Japanese
exports, and a very low share of “quality-competition goods” in Chinese exports. Unit values
can reflect not only quality but also costs (Khandelwal 2010). Idiosyncratic preferences
15
Table 3: Change in world market shares for high-tech products and by market segment,1995-2009
High-tech products Top-range Mid-range Bottom-range
2009 95-09 2009 95-09 2009 95-09 2009 95-09Exporter % p.p. ∆ % p.p. ∆ % p.p. ∆ % p.p. ∆
EU27 18.1 1.55 28.8 -0.89 17.1 -2.64 15.2 -3.20France 3.3 0.10 3.4 -0.63 2.3 -0.73 1.9 -0.51Germany 4.7 0.66 8.8 -0.97 5.1 -0.61 3.4 -0.54Italy 1.2 -0.03 3.1 -0.03 1.9 -0.44 2.1 -0.97United Kingdom 2.0 -0.99 2.9 -0.86 1.7 -0.93 1.5 -1.07Euro Area 12 13.5 1.74 22.7 -0.53 13.2 -1.92 11.2 -2.33
USA 13.4 -9.97 13.0 -5.04 13.9 -2.96 10.5 -6.86Japan 7.3 -12.29 11.0 -8.20 8.8 -9.10 4.2 -5.31Canada 1.9 -0.70 1.8 -0.99 4.7 -0.98 2.4 -3.17Switzerland 2.9 0.56 4.8 -0.35 1.6 -0.85 1.6 0.43
China 21.4 16.62 11.6 8.88 16.4 10.76 22.9 13.18Brazil 0.6 0.33 1.1 0.21 2.3 0.64 1.8 -0.25India 0.9 0.73 1.2 0.78 1.6 0.75 2.7 1.29Indonesia 0.6 0.25 0.9 0.03 1.5 -0.07 1.5 0.08Korea 6.5 1.22 2.8 -0.18 4.6 0.35 6.8 1.75Malaysia 4.1 -0.59 2.3 0.93 1.9 -0.20 2.1 -0.37Mexico 3.2 1.07 1.4 0.49 4.0 2.16 2.5 -1.47Taiwan 7.0 1.56 2.2 0.35 2.5 -0.01 4.1 -0.85Singapore 3.5 -3.41 2.1 -0.77 1.6 -0.48 2.0 0.05Thailand 2.3 0.13 1.9 0.33 2.5 1.00 1.9 -0.29
MENA 1.6 0.74 3.3 1.17 3.7 1.23 4.3 1.92SSA 0.2 0.08 1.3 0.55 1.8 0.07 1.6 0.43RoW 4.5 2.13 8.6 2.70 9.6 0.33 12.0 2.64
Source: Authors’ calculations using BACI values (current USD) of traded goods. We exclude oiland intra-EU trade. The change in market shares is given in percentage points (p.p.). Results forcountries accounting for less than 1% of world exports from 1995 to 2009 are aggregated withinthree groups: the Middle East and North Africa (MENA), Sub-Saharan Africa (SSA), and Rest ofthe World (RoW).
for products’ horizontal attributes may also lead to exports of goods of the same quality
at different prices. Finally, export prices may vary for reasons other than quality or costs
(Hallak & Schott 2011). Our approach is accordingly examining changes in market shares
by price range. If a country’s exports are in the high price range but exhibit quality that
does not deserve such pricing, market shares will shrink.
The procedure we use deserves more explanation since it aims to tackle the within trade
16
flows heterogeneity. We rely on the distribution of unit values for each HS6 product and
year, based on the assumption of a continuum of vertically differentiated products. Notice
first that, for a given exporting country, the HS6 data actually aggregates different flows
under a single heading, reported by several firms on several dates by year. Hence each “flow”
reported by the trade statistics will be difficult to classify under a single vertical specialization
positioning. Accordingly, we rely on a smoother procedure, used by Fontagne et al. (2008),
that splits each elementary trade flow into two adjacent ranges of prices out of the three
considered (low, medium, high). More specifically, if i is the exporter, j the destination
market, k the product, and t the year, the relative unit value of a bilateral flow, noted
r = rijkt, is obtained as the ratio between the bilateral unit value and the trade weighted
geometric average of all unit values in the world for the product and year concerned.14 If
r < 1, then the value allocated to the low range is Xijkt(1 − rα) and the value in medium
range is rαXijkt. If r > 1, then the value allocated to the high range is Xijkt(1−1/rα) and the
value allocated to the medium range is Xijkt(1/rα). The lower α is, the higher the share of
trade in the medium range (here we use α = 4 to end up with similar size groups).15 Overall,
we decompose each bilateral value (Xijkt) across an additional dimension s, corresponding
to the market segment (s = bottom,mid−, top).
Implementing this procedure, we observe the market positioning of exported products,
as described in Table 3. The remaining three pairs of columns in this Table give the world
market shares in 2009, and their change in percentage points over the period 1995-2009 for
each of the three market segments (bottom, middle, top). EU’s leadership for top-range
exports is ascertained, with almost 29% of the world market. The EU has a market share
that is almost twice as high for top range products compared to those in the middle or
lower range. The United States and Japan exhibit a quite different pattern, with similar
world market shares in top- and mid-range products and smaller market shares in bottom
14Noting UV the unit values and V the trade values used as weights, the relative unit value is:
r = rijkt =UVijkt
(∏
ij UVVijkt
ijkt )1/∑
ij Vijkt
15Since quantities are not systematically reported, we assume that non allocated flows (in terms of unitvalues) are distributed by market segment in the same way as allocated flows.
17
range products. Both countries are losing ground in all ranges of products. By contrast, the
resilience of the EU market share for top range products is remarkable, with less than one
percent point of world market lost over the whole period. An in-depth look shows that this
loss occurred during the crisis, in the period 2008-2009. Chinese gains are concentrated in
the middle and the bottom segments of the market, although Chinese exporters (actually
mostly foreign firms assembling in China) have started to gain market shares in the upper
segment of the market.
The evidence provided so far is purely descriptive. We cannot identify the pure perfor-
mance of exporting countries on this basis, as changes in market shares can be also driven
by composition effects. The next section aims to disentangle composition effects from pure
competitiveness. This will be done for different ranges of vertically differentiated varieties of
traded products.
3 An econometric shift-share analysis of export growth
This section aims to identify the contributions to export growth: what are the product
and market composition effects and what stems from pure competitiveness? One of the
simplest ways to investigate growth rates is the shift-share approach, also known as the
constant market share (CMS) analysis or structural decomposition. Fabricant (1942) and
Maddison (1952) were among the first to formalize the shift-share decomposition, which was
extensively used afterwards. Although employed mainly in regional studies on employment
and productivity growth, this technique has been successfully extended to international trade
issues over the last six decades (Tyszynski 1951, Richardson 1971a,b, Fagerberg 1988). The
method has been extensively used in competitiveness studies. Laursen (1999), Worz (2005),
Brenton & Newfarmer (2007), and Cafiso (2009) are examples of papers that use a structural
decomposition to analyse export performances at the country level. In the context of the
recent economic crisis it gained interest among central bank researchers (ECB 2005, Amador
& Cabral 2008, Jimenez & Martın 2010, Panagiotis et al. 2010, Finicelli et al. 2011).
Instead of following this traditional decomposition, we adopt an econometric approach,
taking advantage of the data disaggregation. In addition, in order to capture variations
18
across time, we focus on the sum of annual growth in each trade flow rather than on the
increase in its value between the first and last year of the considered period. Our method is
therefore constrained by the observation of the same flow in two consecutive years (necessary
for computing annual growth rates). As in panel 2 of Table 1, we exclude flows under USD
10,000 and those concerning micro-states. The 3.9 million flows that satisfy these conditions
account for a trade growth of bn USD 4,095. This figure does not include trade flows created
(bn USD 353) or that disappeared (bn USD 289) during the period, and is larger than the
intensive margin of panel (1) in Table 1. As previously, market positioning in terms of
technology or quality is computed from HS6 level data. However, in order to capture even
more trade flows in the intensive margin, the decomposition of export growths is performed
on data aggregated to the 2-digits level of the HS classification.
3.1 The shift-share methodology applied to changes in market
shares
In the field of international trade, the CMS or shift-share analysis aims to measure the
contribution of countries’ geographical and sectoral specialization to the growth of their
exports. Since the analysis is performed on export growth, only the intensive margin of trade
is explained. The method is simply to compute the contribution of the initial geographical
and sectoral composition of exports to changes in market shares. The remaining proportion
of the change is attributed to pure performance (i.e. price and non-price competitiveness).
The traditional shift-share analysis is based on an algebraic decomposition of the total
export growth of a country (or a region) during a given time period. Four contributions are
identified, namely world trade growth, growth in exports of individual products (sectoral
effect), growth in specific markets’ imports (geographical effect), and a residual performance
of the exporter.16 When market shares are considered instead of export growth, as is the case
16The following equation gives this identity:
Xti.. −Xt−1
i.. = rXt−1i.. +
∑k
(rk − r)Xt−1i.k +
∑jk
(rjk − rk)Xt−1ijk +
∑jk
(Xt
ijk −Xt−1ijk (1 + rjk)
)where i denotes the exporter, j the importer, k the product or sector, t the time period, r the global growthrate of exports for all countries in the sample except i, rk the global growth rate of product k exports, andrjk the global growth rate of exports of product k to country j.
19
in this study, there are three components rather than four. Such structural decomposition
has a major drawback: results are sensitive to the order in which the composition effects
are considered. Computing sectoral effects first and geographical effects afterwards and vice
versa yields different results.
Departing from this traditional analysis, we rely here on a shift-share methodology based
on econometrics, proposed by Cheptea et al. (2005), which is a further development of the
weighted variance analysis of growth rates of Jayet (1993).17 The aim of this method is
ultimately to decompose the growth of each country’s world market shares into three terms:
a geographical structure effect, a sectoral effect, and an exporter-effect which represents
the exporter’s performance. To compute country-level structural and performance effects,
we first explain the growth rate of each individual trade flow (from each exporter to each
importer for a given product and year) and, in a second step we aggregate results at the
exporter level.
Let wt denote the average weight of a flow in world trade in years t − 1 and t: wtijk =
12
(Xt−1
ijk
Xt−1 +Xt
ijk
Xt
)and wti = 1
2
(Xt−1
i
Xt−1 +Xt
i
Xt
). The bilateral and sectoral export growth rates are
regressed on dummies identifying exporters (i), importers (j) and HS2 groups of products
(k) with weighted (by wtijk) OLS:
ln
(X tijk
X t−1ijk
)= interceptt + αti + βtj + γtk + εtijk. (1)
where X represents the value of exports, βtj and γtk capture the contribution of the average
geographical and product structure in year t to the annual growth rate of exports between
t−1 and t, αti is the amount of growth in t that can be attributed to the export performance
of country i, and interceptt is a constant term. More than half of the fixed effects exhibit
an absolute value of the t-test greater than 2 (the distributions are plotted in Figures 2 to 4
in the Appendix). The above decomposition is done for each year between 1995 and 2007.
We thus estimate thirteen annual effects for each exporter, importer and product.18
Unlike Cheptea et al. (2005), the growth rate of country i’s exports is computed here as
17The traditional shift-share analysis is actually a constrained and imperfect version of regression andvariance analysis techniques.
18Data on 1994 flows serve as base year for 1994-1995 growth rates.
20
the logarithm of the Tornqvist index of its exports of each product k to each partner j.19
The annual growth of country i’s exports in period t is obtained as an approximation of the
true logarithmic change in its exports:
d lnX ti = ln
(X ti
X t−1i
)≈∑jk
wtijkwti
ln
(X tijk
X t−1ijk
). (2)
Thus, we express the growth of country i’s exports as a weighted average of the logarithmic
change in its exports of each product k to each partner j.20
Combining equations (1) and (2), we can express the overall growth of country i exports
in terms of the three types of effects mentioned above:
d lnX ti = interceptt + αti +
∑j
wtijwti
βtj +∑k
wtikwti
γtk. (3)
To reach equation (3) we use the fact that the weights of all flows involving exporting country
i add up to the weight of its exports in world trade, wti =∑
jk wtijk, and that the sample
weighted average of the error term in (1) is equal to zero,∑
jk wtijk ε
tijk = 0.21 Given the
large size of our sample (over 200,000 observations per year), the identity established by (3)
is almost unaltered if we replace the constant term, exporter, importer, and product effects
by their OLS estimates.
Let hats indicate OLS-estimated coefficients in (1). When estimating (1), one individual
for each set of fixed effects has to be removed because of collinearity. Therefore, αti is a
measure of country i’s ‘pure’ export growth relative to the omitted country. A measure
of country i’s effect independent of the choice of the omitted country is given by the least
square mean (hereafter LSMEAN ), obtained by adding the intercept and the weighted mean
19The Tornqvist index is the weighted geometric average of the relative change between the current andbase period where weights are the arithmetic average of the market shares in the two periods.
20Although at the exporter/importer/product level the difference between growth rates computed accord-ing to the two sides of the above equation may vary significantly, the weighted averages at the level of eachexporter are very similar. For example for France the difference between the two weighted means representsat most 6% of the largest of the two values. For Germany the difference is even smaller.
21The last constraint is implicitly imposed when estimating (1) with weighted OLS.
21
of partner and product effects to the estimated effect:
LSMEAN ti = αti + ˆintercept
t+∑j
wtj βtj +∑k
wtk γtk. (4)
Note, that the weighted average of country-specific ‘pure’ export growth gives the growth
rate of world trade:∑
iwtiLSMEAN t
i =∑
ijk wtijk ln
(Xt
ijk
Xt−1ijk
)= d lnX t. We employ the fact
that the sum of weights across any dimension is equal to one(∑
iwti =
∑j w
tj =
∑k w
tk = 1
)to establish this result.
For similar reasons, we normalise the estimated importer and product effects. The new
values are obtained by subtracting the weighted average of estimated effects from the param-
eters estimated originally: βtj = βtj −∑
j wtjβ
tj and γtk = γtk −
∑k w
tkγ
tk. Note that with these
notations equation (1) becomes ln
(Xt
ijk
Xt−1ijk
)= LSMEAN t
i+ βtj+ γtk+εtijk. The decomposition
(3) can then be re-written as:
d lnX ti = LSMEAN t
i +∑j
wtijwti
βtj +∑k
wtikwti
γtk. (5)
The first right-hand side element of (5) represents the export performance of country i. The
last two terms reflect the contribution of its exports structure by partner and product to the
overall growth of its exports. We refer to them as the geographical and sectoral structure
effects.
We thus decompose the growth of each country’s exports into three terms: an exporter
(performance) effect, a geographical structure effect which depends on the destination of
exports, and a sectoral effect that varies with the sectoral composition of exports. The
decomposition of export growth is carried out separately for each year. Note that the sum
of annual growth rates yields the change in the value of exports between the first and last
year of the period. Therefore, results for the entire 1995-2007 period are obtained by adding
together the different effects across years:
d lnX95−07i ≡
∑t
d lnX ti =
∑t
LSMEAN ti +
∑t
(∑j
wtijwti
βtj
)+∑t
(∑k
wtikwti
γtk
). (6)
22
Let us consider an illustrative example. According to our methodology, the growth of
Chinese exports in 2000 (relative to 1999) is equal to the sum of the Chinese export per-
formance in 2000, the effect of the average geographical orientation and that of the average
product composition of Chinese exports in 2000. The 1995-2007 growth in exports from
China is the sum of these three effects computed for each year of the period.22
Now, we can transpose this decomposition into a decomposition of changes in market
shares. For this, we subtract from both the left and right-hand side expressions of (6)
the logarithmic change in world exports over the period computed as a Torqvist index,
d lnX95−07, and take the exponentials of the resulting expressions.23 We obtain:
g95−07i ≡ exp
(d lnX95−07
i − d lnX95−07)− 1 = PERFi × GEOi × SECTi − 1 (7)
where PERFi = exp (∑
t LSMEAN ti − d lnX95−07), and GEOi and SECTi are the expo-
nentials of the last two terms of the right-hand side expression of equation (6). Note that
d lnX95−07i and d lnX95−07 are approximations of true logarithmic changes in country
and world exports obtained with the Tornqvist index.24 Therefore, g95−07i in equation (7) is
an approximation of the actual market share growth rate.25
Exporting countries have no influence on structural effects affecting their exports. These
effects result from the growth in destination markets, given the geographical and sectoral
composition of exports. In contrast, the performance effect is a true competitiveness effect.
It indicates the degree to which the exporting country has been able to gain or lose market
shares, after controlling for composition effects.
22Figures corresponding to this example are displayed in the upper part of Table ??.
23Accordingly, we have d lnX95−07 ≡∑t
(d lnXt) =∑t
(∑i
wti d lnXt
i
).
24d lnX95−07i ≈ ln
(X2007
i /X1995i
)and d lnX95−07 ≈ ln
(X2007/X1995
).
25Actual (true) market share growth rates are obtained as(
X2007i
X2007 − X1995i
X1995
)/(
X1995i
X1995
).
23
3.2 Contributions to the changes in world market shares: all prod-
ucts
We now report the results of the shift-share analysis. We explain the annual growth of all
trade flows existing in any two consecutive years and aggregate results in terms of market
shares over the period 1995-2009.26 The estimation is performed at the 2-digit level of the HS:
the 6-digit level does not give very different results, while the HS2 secures higher statistical
significance of parameter estimates. However we continue to define unit values ranges and
technological products at the HS6 level. The statistical significance of fixed effects αti, βtj,
and γtk by year is shown in Figure 2 in the Appendix.
Table 4 shows the differences between market shares considered in this section and those
in section 2. The first column in Table 4 reports the changes in market shares between
1995 and 2009 as presented in Table 2 (e.g. the EU25 loses 1.3 p.p. of the world market
shares). The following three columns consider the change in world market shares by focusing
on the intensive margins of trade only and excluding minor flows, i.e. using the exact sample
on which we perform the shift-share analysis. Column (2) gives changes in market shares
computed on flows existing in any two consecutive years. Note that the difference between
column (1) and column (2) is negligible for all countries. This indicates that the change
in market shares for the shift-share sample is a good proxy of the change in market shares
computed from all trade flows. Column (3) provides the same information as column (2),
but here expressed in percentage terms (the 1.49 p.p. loss of the EU25 represents 7.2% of
the value of its exports in 1995). Column (4) displays the change in world market shares as
computed with the Tornqvist index, i.e. g95−07i from equation (7). It is this change that is
decomposed by our shift-share analysis (last three columns).
To clarify the difference between the different columns of Table 4, let us consider the case
of Chinese exports. In 1995 Chinese exports represented only 6.3% of the value of world
trade; they increased by the year 2009 by 10.80 p.p. When we exclude the extensive margin
(flows that appeared and dissapeared over the period) and minor flows, the market share
26As mentioned above, the sample used eliminates the noise associated with very small values (below USD10,000), non-independent territories and micro-states, and drops HS sections 25, 26, 27, 97, 98, 99 (mineral,specific and non-classified products).
24
Table 4: Changes in world market shares for large exporters (overall growth and intensivemargin) and shift-share decomposition, 1995-2009
Overall Intensive margin Shift-share
p.p., panel (1) p.p. % % Structural Effects Export
of Table 1 panel (2) of Table 1 eq.(7) geographical sectoral performance
(1) (2) (3) (4) (5) (6) (7)
EU27 -1.30 -1.49 -7.2 -5.4 7.4 9.7 -19.7France -0.38 -0.41 -14.4 -13.2 10.0 16.8 -32.5Germany -0.16 -0.17 -3.1 -1.1 6.2 10.1 -15.5Italy -0.43 -0.45 -16.5 -15.0 11.7 -6.0 -19.0UK -0.89 -0.90 -32.1 -34.7 1.4 17.8 -45.4Euro Area -0.79 -0.88 -5.6 -3.6 7.6 9.1 -18.0
USA -5.76 -5.79 -31.7 -31.2 4.8 9.9 -40.3Japan -6.17 -6.18 -43.6 -44.0 0.5 6.3 -47.6Canada -2.17 -2.17 -41.4 -40.6 -22.5 -0.3 -23.1Switzerland -0.37 -0.38 -13.5 -9.4 -0.6 25.3 -27.2
China 10.80 10.76 171.1 180.9 -15.1 -20.8 317.3Brazil 0.29 0.23 15.9 22.4 -1.8 -11.3 40.5India 1.02 0.99 91.2 98.3 5.9 -16.6 124.4Indonesia 0.11 0.10 8.3 13.3 -6.7 -21.8 55.3Korea 0.89 0.71 18.7 21.7 8.4 -0.8 13.1Malaysia -0.29 -0.31 -12.7 -11.4 -8.4 -1.3 -1.9Mexico 0.46 0.45 20.4 23.0 -23.0 -0.9 61.1Taiwan -0.44 -0.50 -13.5 -13.7 14.6 -4.5 -21.2Singapore -0.73 -0.72 -26.2 -20.4 5.1 8.6 -30.2Thailand 0.32 0.32 17.7 20.4 -5.1 -10.5 41.8
MENA 1.44 1.60 64.4 61.6 14.0 -10.4 58.2SSA 0.06 -0.02 -1.1 -4.6 -0.9 -8.4 5.1RoW 1.84 0.94 11.6 11.6 3.4 -13.6 24.9
Source: Authors’ calculations using BACI database. Figures in column (1) are obtained using the sampleof the panel (1) of Table 1. The difference between columns (1) and (2) are due to the exclusion of theextensive margin and tiny trade flows (below USD 10,000, involving non-independent territories and micro-states) in the latter. Column (3) provides the same information as column (2), but here expressed as a% change relative to the 1995 market share. Columns (4) is the approximation of the Tornqvist index.The shift-share estimation is performed at the 2-digit level of the HS (figures are expressed in terms ofpercentage change in market share). The last four columns correspond to gi · 100, (SECTi − 1) · 100(GEOi−1) ·100 and (PERFi−1) ·100 respectively, from equation (7). Results for countries accountingfor less than 1% of world exports from 1995 to 2009 are aggregated within three groups: the Middle Eastand North Africa (MENA), Sub-Saharan Africa (SSA), and Rest of the World (RoW).
25
growth is almost unchanged (10.76 p.p.), which represents 171.1%. When annual changes
in exports are approximated using a Tornqvist index (column 4), we obtain a growth rate
of 180.9%. In the following, we will compute the contributions of sectoral, geographical and
performance effects to this 180.9% increase.
Columns (5) to (7) of Table 4 show our decomposition of changes in market shares
computed using the Tornqvist index for all products taken as a whole over the entire period
(1995-2009). The 7% loss of world market share by the EU25 results solely from the negative
performance effect, since the geographical and sectoral structures both contributed positively
to the growth of European exports. Disentangling “old” and “new” EU Member States
points to the positive contribution of the latter to the overall European export performance.
More generally, the individual performances of Member States are very different: the Irish
performance, as well as that of most new Member States, is striking and contrasts with
the difficulty faced by the UK, France, Denmark, Belgium-Luxembourg, and Sweden. Of
the EU15, only Greece, Portugal, Italy, and Spain suffer from a poor sectoral specialization
(Table 10 in the Appendix). Lastly, the euro area performs slightly better than the EU27,
which implies bad export performances for European countries not using the euro (UK shows
the largest losses with almost 30% between 1995 and 2007).
However, the magnitude of the EU’s losses (even EU15 ones) is much more limited than
those recorded by Japan and the US. Structural effects contribute positively to the growth
in American market shares but negative performance effects are stronger. Japanese losses
in market shares are particularly strong (notably in the sub-period 2001-2009), with only
sectoral specialization contributing positively. All in all, the EU’s performance remains sat-
isfactory given the pressure of new competitors: China, but also India, Mexico or Indonesia,
show impressive export performances, although negative structural contributions in general.
This resilience of EU’s market shares is largely due to Germany’s resilience and, to a lesser
extent, to new Member States performances as is shown in Table10, which details the results
for individual EU27 countries.27 Moreover, the EU’s losses are smaller in volume terms (Ta-
27The CMS analysis from Crespo & Fontoura (2010), which uses a panel similar to ours, also providesevidence of the growth of market share of many emerging countries in Asia and Central and Eastern Europe,despite their negative sector and /or geographical structure effects. As confirmed by Beltramello et al. (2012)using our methodology and data, the sectoral effect is negative for most emerging exporters, reflecting theirspecialization toward more traditional, lower technology industries.
26
ble 11 in the Appendix), indicating a negative price effect, in particular for Germany and
France.
As noted above, since the great trade collapse was synchronised among exporters, aggre-
gate figures do not change the trend observed since 1995: advanced exporters continued to
lose their market shares to the benefit of emerging ones, during and after the crisis. However,
France and Switzerland post better performances when these last two years are included in
the sample, mostly due to changes in sectoral demand, positively affecting their sectoral
effect. Conversely, Japan, the US and Canada increase their losses in the last two years,
combining worse performance and less favorable sectoral effects. Estimated HS2 fixed-effects
indeed significantly change year by year: in particular, considering the period 1995-2009 or
excluding the years 2008 and 2009, as shown in Figure 3 in the Appendix, does not give the
same average effects.
3.3 Focus on high-tech and top range products
We now consider the changes in world market shares for high-tech products and top range
products. As in Section 2.3, these two aspects are considered separately. High-tech products
are defined at the most detailed level of the product classification, regardless of their market
positioning in terms of unit values. In addition, we rank individual countries exports in three
price segments of the world market, considering all products, whatever their technological
level, and taking unit values of trade flows. The decomposition is still performed at the HS2
level.
Regarding high-tech products, the results are reported in Table 5. We observe a 12.6%
increase in the EU’s world market share. This increase is the result of the favourable sectoral
positioning of European exporters, albeit dampened by their disappointing performance on
dynamic foreign markets.28 In contrast, the US and Japan lose about half of their 1995
market shares over the decade, due to a massive relocation of their assembly lines to Asia,
particularly China. The share losses of developed countries are mirrored by large gains
recorded by many developing countries. China, Brazil and India stand out with the best
28The performance of the EU25 on high-tech products is considerably better than that of the EU15. NewMember States combine positive structure effects with a strong performance effect.
27
Table 5: Shift-share decomposition of the percentage changes in world market shares, 1995-2009: technological products
% ∆ Contribution of:
in market share Export Structure effects
using eq. (7) Performance Geographic Sectoral
EU27 12.6 -20.3 2.7 37.5France -0.3 -41.1 10.2 53.7Germany 27.2 -5.9 2.8 31.5Italy -2.5 -33.6 1.6 44.6United Kingdom -35.9 -52.7 -5.6 43.4Euro Area 17.3 -18.5 3.5 39.2
USA -43.5 -52.9 4.3 14.9Japan -63.3 -63.5 8.3 -7.1Canada -26.9 -18.8 -26.7 22.7Switzerland 23.3 -38.8 -5.0 112.0
China 353.5 623.5 -16.0 -25.4Brazil 212.9 188.9 -10.6 21.2India 361.3 154.8 13.2 59.9Indonesia 72.2 151.8 -12.5 -21.8Korea 25.4 34.7 9.7 -15.1Malaysia -12.8 27.2 -9.5 -24.3Mexico 51.9 151.6 -30.4 -13.2Taiwan 26.0 21.9 21.1 -14.6Singapore -49.5 -45.0 11.4 -17.6Thailand 7.8 55.7 -7.4 -25.2
MENA 60.8 38.1 -1.3 17.9SSA -18.0 -21.9 -15.6 24.5RoW 89.5 85.7 2.0 0.1
Source: Authors’ calculations using all trade flows from BACI database recorded in any twoconsecutive years in the considered period, except flows associated with HS sections 25, 26,27, 97, 98, 99, very small values (below USD 10,000), non-independent territories and micro-states. The estimation is performed at the 2-digit level of the HS. All figures are expressedin terms of percentage change in market share. The four columns correspond to gi · 100,(PERFi − 1) · 100, (GEOi − 1) · 100 and respectively (SECTi − 1) · 100 from equation(7). Results for countries accounting for less than 1% of world exports from 1995 to 2009 areaggregated within three groups: the Middle East and North Africa (MENA), Sub-SaharanAfrica (SSA), and Rest of the World (RoW).
28
performances, multiplying their initial market shares by four, more than three and more
than two respectively.
The decomposition of changes by market segment, raises an additional data issue. In
order to fully capture year-on-year changes in market share for a given exporter, one must
take into account the fact that some flows may be classified in two different market segments
depending on the year. If the computation of the growth rates were performed on flows
classified at both dates in the same market segment, these shifters would not be present. To
overcome this problem, we adopt the following strategy. For each trio (exporter, importer,
HS6) and year we classify:29 As middle range products, flows present in the top range in t1
but not in t0; as middle range products, flows present in the top range in t0 but not in t1;
other shifters as bottom range products.
We now shift to Table 6, focusing on the upper segment of the world market. For the EU,
the growth in market share for top-range products (+7%) contrasts with the global result
(-5.4% in Table 4) and suggests a rise in the unit values of European exports. This is mostly
due to the sectoral structure: the EU has benefited from a composition effect, whereby world
demand has increased faster for its most exported top-range products. But the European
export performance is also less negative (it is even positive for the Euro area), whereas is
still very negative for Japan and the US. Here again the difference with the new Member
States is striking, even if these percentage changes apply to tiny market shares. Contrasting
with the EU and the US, Japan has benefited from a favourable geographical orientation
of their exports of top-range products, thanks to a larger orientation toward a fast growing
Asian market.
4 Conclusion
In the context of a profound reshaping of world trade flows starting in the mid-1990s, we
observe that the redistribution of market shares observed between emerging and developed
countries – and among developing countries themselves – has affected the EU, Japan and
the US differently. EU managed to maintain its world market share at 19.4% for goods
29Non-shifters (e.g. top range in t0 and t1) are indeed kept in their initial range.
29
Table 6: Shift-share decomposition of the percentage changes in world market shares, 1995-2009: top-range products
% ∆ Contribution of:
in market share Export Structure effects
using eq. (7) Performance Geographic Sectoral
EU27 7.0 -1.3 -0.4 8.9France -1.4 -14.6 2.0 13.3Germany 7.3 2.6 3.5 1.1Italy -5.0 13.5 4.3 -19.7United Kingdom -19.6 -32.8 -0.9 20.6Euro Area 10.5 3.8 -0.9 7.3
USA -26.4 -28.8 -6.0 10.1Japan -25.7 -32.2 14.6 -4.4Canada -50.4 -41.0 -15.0 -1.0Switzerland -6.4 -29.0 0.2 31.6
China 187.5 436.7 -23.4 -30.1Brazil 27.1 44.1 -14.8 3.5India 40.8 61.2 -0.2 -12.5Indonesia -10.3 37.5 -6.0 -30.6Korea 6.1 26.9 3.6 -19.3Malaysia -20.3 1.7 -5.8 -16.8Mexico 44.6 61.5 -8.6 -2.0Taiwan -6.2 0.6 21.5 -23.3Singapore -37.8 -50.7 19.8 5.4Thailand -12.5 25.1 -9.3 -22.8
MENA 50.8 69.9 9.2 -18.7SSA 25.2 41.4 -7.4 -4.4RoW 19.9 25.1 4.1 -8.0
Source: Authors’ calculations using all trade flows from BACI database recorded in anytwo consecutive years in the considered period, except flows associated with HS sections25, 26, 27, 97, 98, 99, very small values (below USD 10,000), non-independent territoriesand micro-states. The estimation is performed at the 2-digit level of the HS. All figures areexpressed in terms of percentage change in market share. The four columns correspond togi ·100, (PERFi−1) ·100, (GEOi−1) ·100 and (SECTi−1) ·100 respectively fromequation (7). Results for countries accounting for less than 1% of world exports from 1995to 2009 are aggregated within three groups: the Middle East and North Africa (MENA),Sub-Saharan Africa (SSA), and Rest of the World (RoW).
30
(excluding energy and intra-EU trade) losing only 1.3 percentage points over the period
(1995-2009). Market share losses are considerably larger in the case of the United States
and Japan with a decline of around 6 percentage points. The US and Japan now account
for 12.5% and 8.0% of world market shares respectively.
Our analysis of the intensive and extensive change in the value of world trade shows that
although the exports of new products and/or exports to previously unexploited markets
account for a large share of the total number of flows both in 1995 and 2009, they represent
only 17% of the increase in global trade in value terms. The contribution of the intensive
margin to the growth in the value of exports of all developed countries is large, pointing to
a relative inertia in the orientation of European, American and Japanese exports.
Our shift-share analysis of export growth shows that European losses recorded between
1995 and 2009 are exclusively attributable to a negative contribution of the exporter effect.
By contrast, the geographical and sectoral structure of EU exports contributed positively
to the export growth. Focusing on the EU15 reinforces this conclusion. Sectoral effects are
generally positive for OECD countries and geographical effects are negative for countries in
the Americas and some in Asia.
Regarding high-tech and top-range products, the EU has increased its world market share.
This better positioning of the EU25 among developed countries is due not only to a superior
relative export performance, but also to a more pronounced specialization in products with
rapidly growing import demand.
This paper yields two contributions. From a methodological point of view, our findings
illustrate the advantage of working at the most detailed level of the classification of products
when it comes to defining market segments. These results also illustrate the benefits of
a shift-share analysis applied to the intensive margin of country exports. From a policy
perspective, our results indicate that the EU has withstood better the competition from
the major emerging traders, thanks to buoyant world demand for top range products its
exporters were specialised in.
31
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5 Appendix
5.1 Data description
The trade data used in this paper are from the BACI database, a database for the analysis of
international trade at the product-level developed by Gaulier & Zignago (2010). BACI draws
on the UN COMTRADE information, in which imports are reported CIF (cost, insurance
and freight) and the exports FOB (free on board). BACI provides reconciled FOB data on
trade flows: for a given product k and a given year t, exports from country i to importer j are
equal to j imports from i. This reconciliation of mirror flows is performed for both values and
quantities, and relies on estimated indicators of the reliability of import and export country
reports. The quantity units are converted into tons, making possible the computation of
homogeneous unit values.30
BACI covers trade between more than 200 countries, in the roughly 5,000 products of the
6-digit Harmonised System (HS6) classification. However, this study excludes intra-EU 27
trade flows. This choice must be borne in mind when it comes to market shares and changes
therein. We also exclude mineral, specific and non-classified products.31 Trade flows below
USD 10,000 and involving non-independent territories and micro-states are also excluded in
panel (2) of tables in section 2.1 and in section 3. For the shift-share analysis in section 3 we
employ HS2 data obtained by aggregation of HS6 data. The motivation behind is to keep a
larger share of trade flows in the intensive margin, the only component of the export growth
discussed in that section.
Concerning the high-tech products, we use the classification in broad sectors proposed
by Lall (2000), detailed in Table 7.
The availability of traded unit values at a very disaggregated level (country-partner-
product-year) in the BACI database makes it possible to compute international trade price
indices. Similar to Gaulier et al. (2008) we compute price indices as chained Tornqvist indices
of unit values, but unlike them we compute an index for each pair of trading countries
30BACI is available to COMTRADE users at: http://www.cepii.fr/anglaisgraph/bdd/baci.htm31More precisely, we exclude the six following chapters of the Harmonized System: the mineral products
(chapters 25, 26 and 27), the works of art, collectors’ pieces and antiques (chapter 97) and the two lastchapters, 98 and 99, devoted to special classifications or transactions.
35
(exporter-importer) and HS2 heading. Data in 2000 is taken as reference. We use these
indices to deflate trade values (expressed in current USD in BACI) to obtain trade volumes
expressed in terms of 2000 prices. Since this exercise allows us to disentangle price effects,
we refer to obtained data as volumes.
Table 7: The classification of sectors according to the technological content, Lall (2000)
Classification Examples
Primary products (PP) fresh fruit, meal, rice, cocoa, tea, coffee, wood
Manufactured productsResource based manufactures (RB)
Agro/forest based products Prepared meats/fruits, beverages, wood products, veg-etable oils
Other resource based products Ore concentrates, petroleum/rubber products, cement,cut gems, glass
Low technology manufactures (LT)Textile/fashion cluster Textile fabrics, clothing, headgear, footwear, leather
manufactures, travel goodsOther low technology Pottery, simple metal parts/structures, furniture, jew-
ellery, toys, plastic productsMedium technology manufactures (MT)
Automotive products Passenger vehicles and parts, commercial vehicles, mo-torcycles and parts
Medium technology process industries Synthetic fibres, chemicals and paints, fertilisers, plas-tics, iron, pipes/tubes
Medium technology engineering industries Engines, motors, industrial machinery, pumps,switchgear, ships, watches
High technology manufactures (HT)Electronics and electrical products Office/data processing/telecommunications equip, TVs,
transistors, turbines, power generating equipmentOther high technology Pharmaceuticals, aerospace, optical/measuring instru-
ments, cameras
Other transactions (OT) Electricity, cinema film, printed matter, ‘special’ trans-actions, gold, art, coins, pets
Source: Lall (2000).
The world distribution of unit values for each HS6 heading allows us to classify each
product-bilateral flow into three price segments, and to examine competition among the
main world exporters within each of these segments. Trade flows are ordered according their
unit values and classified as follows: flows with the lowest unit value form the bottom-range,
the ones with intermediate unit values - the mid-market, and the ones with the highest unit
value - the mid-range. We employ the technique developed by Fontagne et al. (2008) to
construct the three market segments. There is also a small “non classified” range of trade
36
flows for which data on trade quantities is not available and unit values cannot be computed,
but they represent less than 10% of world trade.
Tables of this paper display results for countries accounting for more than 1% of world
exports from 1995 to 2009. Results for all other countries in the world are available in our
online appendix.32
32Zipped file at Soledad Zignago’s Banque de France webpage and Lionel Fontagne’s personal webpage.
37
5.2 Additional results
Table 8: Extensive and intensive margins in 1995-2009 for world exports by country, as a %
(1) All trade flows (2) Our (reduced) sample
Intensive Extensive Margin Intensive Extensive MarginMargin + − Margin + −
(Entries) (Exits) (Entries) (Exits)(a) (b) (c) (d) (e) (f)
EU27 97,2 3,6 0,8 99,0 6,5 5,6France 97,7 3,1 0,8 99,6 3,3 2,9Germany 99,7 0,5 0,3 99,7 1,4 1,1Italy 96,3 4,0 0,2 99,2 3,3 2,5United Kingdom 99,6 1,1 0,7 99,3 5,0 4,3Euro Area 12 98,2 2,4 0,6 99,7 4,0 3,7
USA 98,9 1,2 0,1 99,8 1,1 0,9Japan 99,7 0,7 0,4 100,0 1,8 1,8Canada 97,2 3,1 0,3 99,2 4,6 3,7Switzerland 99,0 1,4 0,4 99,9 2,3 2,2
China 99,3 0,8 0,0 99,9 0,3 0,3Brazil 90,1 10,3 0,4 95,3 10,3 5,6India 97,1 3,0 0,1 98,8 3,2 1,9Indonesia 96,6 3,8 0,4 99,0 5,2 4,2Korea 93,8 6,3 0,1 99,5 3,0 2,5Malaysia 97,4 2,8 0,2 98,4 3,9 2,3Mexico 99,4 1,0 0,4 99,5 2,9 2,4Taiwan 92,4 8,2 0,6 96,2 10,0 6,2Singapore 96,7 3,8 0,6 100,8 6,0 6,8Thailand 98,4 1,9 0,3 99,6 2,1 1,6
MENA 86,3 16,0 2,3 107,5 39,2 46,6SSA 76,3 30,0 6,3 92,8 59,8 52,5RoW 69,7 32,2 1,9 88,9 26,3 15,2
Note: Authors’ calculations using BACI values (current USD) of traded goods at the HS 2-digitlevel. The samples used in panels (1) and (2) are those from Table 1. Column (a) refers tothe contribution of export flows (product × destination market) present both in 1995 and 2009.Column (d) refers to the contribution of export flows (product × destination market) present inany two consecutive years from 1995 to 2009. The other columns refer to the contribution ofexport flows appearing (positive contribution) or disappearing (negative contribution) over theperiod. The columns add up as follows: (a)+(b)−(c) = 100 and (d)+(e)−(f) = 100. Results forcountries accounting for less than 1% of world exports from 1995 to 2009 are aggregated withinthree groups: the Middle East and North Africa (MENA), Sub-Saharan Africa (SSA), and Restof the World (RoW).
38
Table 9: Sectoral composition of world and EU exports in 2009 and changes 1995-2009
Sector (ISIC Rev.3) 2009 values, % 95-09, p.p. ∆
World EU World EU
values volumes values volumes
1 Agriculture, hunting 3.4 1.5 -0.43 -0.63 -0.07 -0.052 Forestry, logging 0.2 0.1 -0.18 -0.16 -0.05 -0.055 Fishing & fish farming 0.2 0.1 -0.10 -0.09 0.00 -0.0114 Other mining & quarrying 0.3 0.6 -0.21 0.06 -0.57 -0.0815 Food products & beverages 6.0 5.5 -0.07 -0.45 -1.26 -1.7116 Tobacco products 0.1 0.2 -0.20 -0.40 -0.11 -0.0717 Textiles 2.9 1.5 -0.90 -0.47 -1.37 -1.2118 Wearing apparel 2.6 1.1 -0.51 -0.77 -0.62 -0.8619 Leather 1.3 1.0 -0.35 -0.66 -0.69 -0.9620 Wood & wood products 0.8 0.8 -0.58 -0.58 0.04 0.1321 Pulp, paper & paper products 1.5 1.9 -0.84 -0.39 -0.49 -0.1022 Publishing & printing 0.6 0.8 -0.23 -0.23 -0.34 -0.3924 Chemicals & chemical products 13.3 19.6 2.84 2.01 5.20 3.2425 Rubber & plastic 2.8 2.4 0.30 0.41 0.17 0.2226 Non-metallic mineral products 1.1 1.4 -0.05 0.05 -0.66 -0.6427 Basic metals 8.5 5.7 1.34 -0.35 -0.17 -1.3128 Metal products 2.6 3.0 0.33 -0.11 0.22 -0.3529 Machinery 11.3 17.6 0.46 0.18 -0.32 -2.1330 Office machinery & computers 4.1 1.5 -2.21 -1.64 -1.12 -1.3431 Electrical machinery 4.9 5.5 0.33 0.49 1.00 0.5832 Radio, TV & communication equip. 10.9 4.1 0.56 1.74 -0.88 -2.2233 Medical, precision & optical instr. 4.6 5.0 0.96 1.83 1.30 1.4734 Motor vehicles, trailers & semi-trailers 7.1 9.1 -1.73 -1.88 -0.14 1.1435 Other transport equipment 5.0 6.8 1.09 1.81 1.64 7.2536 Furniture manufacturing n.e.c. 3.8 2.7 0.34 0.23 -0.86 -0.64
Source: Authors’ calculations using BACI values (current USD) of traded goods (intra-EU trade isexcluded). The change in market shares is given in percentage points (p.p.). Since oil is excludedfrom the sample, the“Coke, refined petroleum products & nuclear fuel” industry is not reported here.The sum of reported market shares is exactly 98 and 97% for the world and for the EU respectively.
39
Table 10: Shift-share decomposition of the percentage changes in world market shares, 1995-2009: EU 27 Member States
% ∆ Contribution of:
in market share Export Structure effects
using eq. (7) Performance Geographic Sectoral
EU27 -5,4 -19,7 7,4 9,7Austria 19,4 3,3 8,1 6,9Belgium and Luxembourg -12,7 -36,8 15,2 19,9Bulgaria -35,8 -45,2 25,2 -6,4Cyprus -20,0 -48,1 38,1 11,7Czech Republic 142,5 102,8 24,4 -3,9Denmark -11,8 -21,8 -0,9 13,7Estonia 172,6 160,2 18,5 -11,6Finland -15,4 -26,8 15,6 0,0France -13,2 -32,5 10,0 16,8Germany -1,1 -15,5 6,2 10,1Greece 22,5 12,0 35,5 -19,2Hungary 148,1 98,3 23,9 1,0Ireland 96,5 69,6 -20,7 46,1Italy -15,0 -19,0 11,7 -6,0Latvia 10,5 -4,7 31,7 -12,0Lithuania 25,0 -6,7 44,9 -7,6Malta 71,5 58,7 -1,0 9,1Netherlands -10,4 -26,2 9,7 10,7Poland 145,6 104,1 25,9 -4,4Portugal 4,1 8,0 13,8 -15,3Romania 63,2 29,5 38,9 -9,2Slovakia 441,9 437,7 10,6 -8,9Slovenia 21,2 -22,5 43,3 9,1Spain 15,7 5,3 12,3 -2,1Sweden -23,7 -33,0 3,1 10,5United Kingdom -34,7 -45,4 1,4 17,8
Source: Authors’ calculations using all trade flows from BACI database existing in any two consec-utive years in the considered period, except flows associated with HS sections 25, 26, 27, 97, 98, 99,very small values (below USD 10,000), non-independent territories and micro-states. The estimationis performed at the 2-digit level of the HS. All figures are expressed in terms of percentage changein market share. The four columns correspond to gi · 100, (PERFi − 1) · 100, (GEOi − 1) · 100and (SECTi − 1) · 100 respectively from equation (7).
40
Table 11: Shift-share decomposition of the percentage changes in world market shares, allproducts, 1995-2009: in volume terms
% ∆ Contribution of:
in market share Export Structure effects
using eq. (7) Performance Geographic Sectoral
EU27 -2,5 -11,8 0,8 9,7France 34,0 4,1 2,6 25,5Germany 9,2 -2,8 1,6 10,5Italy -25,8 -23,0 3,6 -6,9United Kingdom -40,7 -44,9 -4,7 12,9Euro Area 12 5,1 -5,6 1,5 9,8
USA -33,3 -45,0 7,4 12,9Japan -40,6 -46,6 2,4 8,7Canada -50,6 -40,5 -21,9 6,5Switzerland -19,5 -36,6 2,1 24,3
China 160,4 307,8 -16,6 -23,5Brazil 15,1 31,3 -0,8 -11,7India 71,3 135,3 1,5 -28,3Indonesia 14,1 54,5 -5,6 -21,8Korea 42,6 31,1 10,1 -1,2Malaysia -15,7 -14,4 -2,9 1,4Mexico 34,8 76,8 -22,5 -1,7Taiwan 32,5 -6,2 39,2 1,5Singapore -16,7 -30,8 9,6 9,8Thailand 12,9 28,3 -3,4 -9,0
MENA 39,8 53,8 8,0 -15,8SSA -7,4 12,0 -0,5 -16,9RoW 1,4 20,7 1,9 -17,5
Source: Authors’ calculations using all trade flows from BACI database existing in any twoconsecutive years in the considered period, except flows associated with HS sections 25, 26,27, 97, 98, 99, very small values (below USD 10,000), non-independent territories and micro-states. The estimation is performed at the 2-digit level of the HS. All figures are expressedin terms of percentage change in market share. The four columns correspond to gi · 100,(PERFi − 1) · 100, (GEOi − 1) · 100 and respectively (SECTi − 1) · 100 from equation(7). Results for countries accounting for less than 1% of world exports from 1995 to 2009 areaggregated within three groups: the Middle East and North Africa (MENA), Sub-SaharanAfrica (SSA), and Rest of the World (RoW).
41
Figure 2: Standard errors of exporter, importer and product fixed effects, central values
02
46
81
0
ab
so
lute
va
lue
of
the
t−
test
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
02
46
81
0
ab
so
lute
va
lue
of
the
t−
test
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
02
46
81
0
ab
so
lute
va
lue
of
the
t−
test
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Source: Authors’ calculations using all trade flows from BACI database existingin any two consecutive years in the period 1995-2009, except flows associated withHS sections 25, 26, 27, 97, 98, 99, very small values (below USD 10,000), non-independent territories and micro-states. The estimation is performed at the 2-digitlevel of the HS.
42
Figure 3: Estimated HS2 fixed effects, 1995-2009 and 1995-2007 periods, values and volumes,by HS sections
-120 -100 -80 -60 -40 -20 0 20 40 60
Pulp of Wood & Paper
Beverages & Tobacco
Miscellaneous Manufactured Articles
Animal or Vegetable Fats & Oils
Arms
Chemicals
Leather
Footwear
Textile
Stone, Cement, Glass & Ceramic Products
Plactics & Rubber
Vegetable Products
Base Metals
Wood
Vehicles & Transport Equipment
Pearls & Precious Metals
Optical, Precision & Medical instruments
Machinery, Electrical Equip., TV & Sound
Animal Products
1995-2009
values
volumes
-120 -100 -80 -60 -40 -20 0 20 40
Pulp of Wood & Paper
Beverages & Tobacco
Miscellaneous Manufactured Articles
Animal or Vegetable Fats & Oils
Arms
Chemicals
Leather
Footwear
Textile
Stone, Cement, Glass & Ceramic Products
Plactics & Rubber
Vegetable Products
Base Metals
Wood
Vehicles & Transport Equipment
Pearls & Precious Metals
Optical, Precision & Medical instruments
Machinery, Electrical Equip., TV & Sound
Animal Products
1995-2007
values
volumes
Source: Authors’ calculations using all trade flows from BACI database existing in any twoconsecutive years in the period 1995-2009, except flows associated with HS sections 25, 26,27, 97, 98, 99, tiny values (below USD 10,000), non-independent territories and micro-states.The estimation is performed at the 2-digit level of the HS.
43
Documents de Travail
380. M. Boutillier and J. C. Bricongne, “Disintermediation or financial diversification? The case of developed
countries,” April 2012
381. Y. Ivanenko and B. Munier, “Price as a choice under nonstochastic randomness in finance,” May 2012
382. L. Agnello and R. M. Sousa, “How does Fiscal Consolidation Impact on Income Inequality?,” May 2012
383. L. Ferrara, M. Marcellino, M. Mogliani, “Macroeconomic forecasting during the Great Recession: The return of
non-linearity?,” May 2012
384. M. Bessec and O. Bouabdallah, “Forecasting GDP over the business cycle in a multi-frequency and data-rich environment,” June 2012
385. G. Dufrénot and K. Triki, “Public debt ratio and its determinants in France since 1890 – Does econometrics
supports the historical evidence? ,” July 2012
386. G. Dufrénot and K. Triki, “Why have governments succeeded in reducing French public debt historically and
can these successes inspired us for the future? An historical perspective since 1890,” July 2012
387. G. Gaballo, “Private Uncertainty and Multiplicity,” July 2012
388. P. Towbin, “Financial Integration and External Sustainability,” July 2012
389. G. Cette, N. Dromel, R. Lecat and A.-C. Paret, “Labour relations quality and productivity: An empirical
analysis on French firms,” July 2012
390. D. Beau, L. Clerc and B. Mojon, “Macro-Prudential Policy and the Conduct of Monetary Policy,” July 2012
391. E. Challe, B. Mojon and X. Ragot, “Equilibrium Risk Shifting and Interest Rate in an Opaque Financial
System,” July 2012
392. D. Fuentes Castro, “Funding for green growth,” August 2012
393. A. Cheptea, L. Fontagné and S. Zignago, “European Export Performance,” August 2012
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